somePairs | R Documentation |
This function lets the user choose one of three criteria to determine causal direction
by setting typ
as 1, 2 or 3. This function reports results for
only one criterion at a time unlike the function some0Pairs
which
summarizes the resulting causal directions for all criteria with suitable weights.
If some variables are â€˜controlâ€™ variables, use someCPairs
, C=control.
somePairs(mtx, dig = 6, verbo = FALSE, typ = 1, rnam = FALSE)
mtx |
The data matrix in the first column is paired with all others. |
dig |
Number of digits for reporting (default |
verbo |
Make |
typ |
Must be 1 (default), 2 or 3 for the three criteria. |
rnam |
Make |
(typ=1) reports ('Y', 'X', 'Cause', 'SD1apd', 'SD2apd', 'SD3apd', 'SD4apd') nameing variables identifying 'cause' and measures of stochastic dominance using absolute values of kernel regression gradients comparing regresson of X on Y with that of Y on X.
(typ=2) reports ('Y', 'X', 'Cause', 'SD1res', 'SD2res', 'SD3res', 'SD4res') and measures of stochastic dominance using absolute values of kernel regression residuals comparing regresson of X on Y with that of Y on X.
(typ=3) reports ('Y', 'X', 'Cause', 'r*X|Y', 'r*Y|X', 'r', 'p-val') containing generalized correlation coefficients r*, 'r' refers to the Pearson correlation coefficient and p-val column has the p-values for testing the significance of Pearson's 'r'.
A matrix containing causal identification results for one criterion.
The first column of the input mtx
having p columns
is paired with (p-1) other columns The output matrix headings are
self-explanatory and distinct for each criterion Cr1 to Cr3.
Prof. H. D. Vinod, Economics Dept., Fordham University, NY
H. D. Vinod 'Generalized Correlation and Kernel Causality with Applications in Development Economics' in Communications in Statistics -Simulation and Computation, 2015, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/03610918.2015.1122048")}
The related function some0Pairs
may be more useful, since it
reports on all three criteria (by choosing typ=1,2,3) and
further summarizes their results by weighting to help choose causal paths.
## Not run:
data(mtcars)
somePairs(mtcars)
## End(Not run)
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